235 related articles for article (PubMed ID: 26262154)
1. Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter.
Zhou X; Coiera E; Tsafnat G; Arachi D; Ong MS; Dunn AG
Stud Health Technol Inform; 2015; 216():761-5. PubMed ID: 26262154
[TBL] [Abstract][Full Text] [Related]
2. Associations Between Exposure to and Expression of Negative Opinions About Human Papillomavirus Vaccines on Social Media: An Observational Study.
Dunn AG; Leask J; Zhou X; Mandl KD; Coiera E
J Med Internet Res; 2015 Jun; 17(6):e144. PubMed ID: 26063290
[TBL] [Abstract][Full Text] [Related]
3. Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data.
Du J; Xu J; Song HY; Tao C
BMC Med Inform Decis Mak; 2017 Jul; 17(Suppl 2):69. PubMed ID: 28699569
[TBL] [Abstract][Full Text] [Related]
4. Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter.
Massey PM; Leader A; Yom-Tov E; Budenz A; Fisher K; Klassen AC
J Med Internet Res; 2016 Dec; 18(12):e318. PubMed ID: 27919863
[TBL] [Abstract][Full Text] [Related]
5. Comparing human papillomavirus vaccine concerns on Twitter: a cross-sectional study of users in Australia, Canada and the UK.
Shapiro GK; Surian D; Dunn AG; Perry R; Kelaher M
BMJ Open; 2017 Oct; 7(10):e016869. PubMed ID: 28982821
[TBL] [Abstract][Full Text] [Related]
6. A natural language processing framework to analyse the opinions on HPV vaccination reflected in twitter over 10 years (2008 - 2017).
Luo X; Zimet G; Shah S
Hum Vaccin Immunother; 2019; 15(7-8):1496-1504. PubMed ID: 31194609
[TBL] [Abstract][Full Text] [Related]
7. Sentiment analysis on smoking in social networks.
Sofean M; Smith M
Stud Health Technol Inform; 2013; 192():1118. PubMed ID: 23920892
[TBL] [Abstract][Full Text] [Related]
8. Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection.
Surian D; Nguyen DQ; Kennedy G; Johnson M; Coiera E; Dunn AG
J Med Internet Res; 2016 Aug; 18(8):e232. PubMed ID: 27573910
[TBL] [Abstract][Full Text] [Related]
9. Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets.
Du J; Xu J; Song H; Liu X; Tao C
J Biomed Semantics; 2017 Mar; 8(1):9. PubMed ID: 28253919
[TBL] [Abstract][Full Text] [Related]
10. Twitter sentiment analysis from Iran about COVID 19 vaccine.
Bokaee Nezhad Z; Deihimi MA
Diabetes Metab Syndr; 2022 Jan; 16(1):102367. PubMed ID: 34933273
[TBL] [Abstract][Full Text] [Related]
11. How to evaluate sentiment classifiers for Twitter time-ordered data?
Mozetič I; Torgo L; Cerqueira V; Smailović J
PLoS One; 2018; 13(3):e0194317. PubMed ID: 29534112
[TBL] [Abstract][Full Text] [Related]
12. What Drives Health Professionals to Tweet About #HPVvaccine? Identifying Strategies for Effective Communication.
Massey PM; Budenz A; Leader A; Fisher K; Klassen AC; Yom-Tov E
Prev Chronic Dis; 2018 Feb; 15():E26. PubMed ID: 29470166
[TBL] [Abstract][Full Text] [Related]
13. Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study.
Boon-Itt S; Skunkan Y
JMIR Public Health Surveill; 2020 Nov; 6(4):e21978. PubMed ID: 33108310
[TBL] [Abstract][Full Text] [Related]
14. Publicly Available Online Tool Facilitates Real-Time Monitoring Of Vaccine Conversations And Sentiments.
Bahk CY; Cumming M; Paushter L; Madoff LC; Thomson A; Brownstein JS
Health Aff (Millwood); 2016 Feb; 35(2):341-7. PubMed ID: 26858390
[TBL] [Abstract][Full Text] [Related]
15. Covid-19 vaccines in Italian public opinion: Identifying key issues using Twitter and Natural Language Processing.
Stracqualursi L; Agati P
PLoS One; 2022; 17(11):e0277394. PubMed ID: 36395254
[TBL] [Abstract][Full Text] [Related]
16. Using Twitter to Understand Public Perceptions Regarding the #HPV Vaccine: Opportunities for Public Health Nurses to Engage in Social Marketing.
Keim-Malpass J; Mitchell EM; Sun E; Kennedy C
Public Health Nurs; 2017 Jul; 34(4):316-323. PubMed ID: 28261846
[TBL] [Abstract][Full Text] [Related]
17. Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models.
Tomaszewski T; Morales A; Lourentzou I; Caskey R; Liu B; Schwartz A; Chin J
J Med Internet Res; 2021 Sep; 23(9):e30451. PubMed ID: 34499043
[TBL] [Abstract][Full Text] [Related]
18. Comparing the Human Papillomavirus Vaccination Opinions Trends from Different Twitter User Groups with a Machine Learning Based System and Semiparametric Nonlinear Regression.
Du J; Huang J; Duan R; Chen Y; Tao C
Stud Health Technol Inform; 2017; 245():1218. PubMed ID: 29295305
[TBL] [Abstract][Full Text] [Related]
19. Population attitudes toward contraceptive methods over time on a social media platform.
Merz AA; Gutiérrez-Sacristán A; Bartz D; Williams NE; Ojo A; Schaefer KM; Huang M; Li CY; Sandoval RS; Ye S; Cathcart AM; Starosta A; Avillach P
Am J Obstet Gynecol; 2021 Jun; 224(6):597.e1-597.e14. PubMed ID: 33309562
[TBL] [Abstract][Full Text] [Related]
20. A novel data-mining approach leveraging social media to monitor consumer opinion of sitagliptin.
Akay A; Dragomir A; Erlandsson BE
IEEE J Biomed Health Inform; 2015 Jan; 19(1):389-96. PubMed ID: 25561458
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]